Prognostic indicators of poor outcomes in pregnant metastatic breast cancer cohort

Abstract

Transcriptomics data from tumor tissue of patients diagnosed with metastatic breast cancer are clustered and associated with overall survival of the patients. A subset of genes from one of the cluster associated with poor outcome are used to generate a survival prediction model predicting a survival time based on expression levels of a plurality of genes. Using such generated survival prediction model, a survival time of a patient diagnosed with metastatic breast cancer can be predicted and a treatment regimen can be updated or generated based on the survival time.

Description

[0001]This application claims priority to our co-pending U.S. provisional applications with the Ser. No. 62 / 521,267, filed Jun. 16, 2017, and Ser. No. 62 / 594,345, filed Dec. 4, 2017.FIELD OF THE INVENTION[0002]The field of the invention is systems and methods of identifying molecular profile of metastatic breast cancer that can be used to predict prognosis and / or survival of metastatic breast cancer patients.BACKGROUND OF THE INVENTION[0003]All publications and patent applications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.[0004]Upon first diagnosis, breast cancer is typically classifie...

AI Technical Summary

Benefits of technology

The patent is about using gene expression data to predict the survival time of patients with metastatic breast cancer. The method involves analyzing transcriptomics data from multiple patients and identifying genes that are associated with poor survival. These genes are then used to create a survival prediction model that can predict the survival time of new patients based on their expression levels. The model takes into account factors like age and disease stage and can be used to improve treatment outcomes for patients with metastatic breast cancer.

Problems solved by technology

However, where the breast cancer is metastatic breast cancer, patients often have a very unfavorable prognosis, despite novel targeted therapies.

Moreover, prognostic and predictive factors for patients with advanced / metastatic breast cancer are not well understood.

Indeed, a molecular assessment of patients and tumors in a metastatic setting is not routinely performed, despite advances in molecular precision medicine indicating great benefit to this patient group.

Thus, even though various systems and methods for classification of breast cancer are known in the art, molecular characterization of metastatic breast cancer is not well understood.

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